Analytical Study of Colour Spaces for Plant Pixel Detection
AbstractSegmentation of regions of interest is an important pre-processing step in many colour image analysis procedures. Similarly, segmentation of plant objects in digital images is an important preprocessing step for effective phenotyping by image analysis. In this paper, we present results of a statistical analysis to establish the respective abilities of different colour space representations to detect plant pixels and separate them from background pixels. Our hypothesis is that the colour space representation for which the separation of the distributions representing object and background pixels is maximized is the best for the detection of plant pixels. The two pixel classes are modelled by Gaussian Mixture Models (GMMs). In our statistical modelling we make no prior assumptions on the number of Gaussians employed. Instead, a constant bandwidth mean-shift filter is used to cluster the data with the number of clusters, and hence the number of Gaussians, being automatically determined. We have analysed the following representative colour spaces:
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Kumar, P.; Miklavcic, S.J. Analytical Study of Colour Spaces for Plant Pixel Detection. J. Imaging 2018, 4, 42.
Kumar P, Miklavcic SJ. Analytical Study of Colour Spaces for Plant Pixel Detection. Journal of Imaging. 2018; 4(2):42.Chicago/Turabian Style
Kumar, Pankaj; Miklavcic, Stanley J. 2018. "Analytical Study of Colour Spaces for Plant Pixel Detection." J. Imaging 4, no. 2: 42.
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